Volume 2, Issue 4, December 2019, Page: 52-59
Conflict Resolution: Analysis of the Existing Theories and Resolution Strategies in Relation to Face Recognition
Akeem Alabi, Department of Computer Science and Engineering, Oduduwa University, Ipetumodu, Nigeria
Babajide Afolabi, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Bernard Akhigbe, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Adewole Ayoade, Department of Computer Science and Engineering, Oduduwa University, Ipetumodu, Nigeria
Received: Nov. 13, 2019;       Accepted: Nov. 26, 2019;       Published: Dec. 24, 2019
DOI: 10.11648/j.ajcst.20190204.12      View  262      Downloads  74
Abstract
A scenario known as conflict in face recognition may arise as a result of some disparity-related issues (such as expression, distortion, occlusion and others) leading to a compromise of someone’s identity or contradiction of the intended message. However, addressing this requires the determination and application of appropriate procedures among the various conflict theories both in terms of concepts as well as resolution strategies. Theories such as Marxist, Game theory (Prisoner’s dilemma, Penny matching, Chicken problem), Lanchester theory and Information theory were analyzed in relation to facial images conflict and these were made possible by trying to provide answers to selected questions as far as resolving facial conflict is concerned. It has been observed that the scenarios presented in the Marxist theory agree with the form of resolution expected in the analysis of conflict and its related issues as they relate to face recognition. The study observed that the issue of conflict in facial images can better be analyzed using the concept introduced by the Marxist theory in relation to the Information theory. This is as a result of its resolution strategy which tends to seek a form of balance as result as opposed to the win or lose case scenarios applied in other concepts. This was also consolidated by making reference to the main mechanisms and result scenario applicable in Information theory.
Keywords
Conflict Resolution, Conflict Theory, Facial Images, Disparity, Expression, Recognition
To cite this article
Akeem Alabi, Babajide Afolabi, Bernard Akhigbe, Adewole Ayoade, Conflict Resolution: Analysis of the Existing Theories and Resolution Strategies in Relation to Face Recognition, American Journal of Computer Science and Technology. Special Issue: Facial Disparity. Vol. 2, No. 4, 2019, pp. 52-59. doi: 10.11648/j.ajcst.20190204.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
N. Eilidh and J. Rob, (2019). Deliberate Disguise In Face Identification. Journal of Experimental Psychology: Applied. ISSN 1076-898X. https://doi.org/10.1037/xap0000213
[2]
N. Katz and K. McNulty, (1994). Conflict Resolution. http:// www.academia.edu.com. (Accessed: 15/01/2019)
[3]
Z. Pawlak, and A. Skowron, (2006). Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland.
[4]
F. Al-Osaimi, M. Bennamoun and A. Mian, (2009). An Expression Deformation Approach To Non- Rigid 3d-Face Recognition. International Journal of Computer Vision. 81: 302–316. DOI 10.1007/s11263-008-0174-0.
[5]
A. Jorstad, D. Jacobs and A. Trouve (2011). A Deformation and Lightening Insensitive Metric For Face Recognition Based On Dense Correspondences. University of Maryland, College Park.
[6]
N. Raajan, M. Ramkumar, B. Monisha, C. Jaiseeli and S. Prasanna (2012). Disparity Estimation From Stereo Images. International Conference on Modeling and Computing. (Elsevier) Procedia Engineering. 38 (2012) 462-472. www.sciencedirect.com.
[7]
A. A. Alabi, (2013). A Modified Principal Component Analysis Technique For Recognizing African Bust. International Journal Of Engineering And Science (IJES); ||Volume||2 ||Issue|| 9||Pages|| 116-129||2013|| ISSN (e): 2319 – 1813 ISSN (p): 2319-1805.
[8]
W. Kandlikar, T. Laxman and D. Jagannath (2014). Conversion of 2D image into 3D and Facial Recognition- Based Attendance System. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization). Vol. 3, Issue 6.
[9]
A. Arpit, K. Abhishek and A. Leena (2014). Study on Advancement In Face Recognition Technology. Proceedings of National Conference on Recent Advances in Electronics and Communication Engineering (RACE-2014).
[10]
A. A. Alabi, L. A. Akanbi and A. A. Ibrahim, (2015). Performance Evaluation of the Eigenface Algorithm on Plain-Feature Images in Comparison with Those of Distinct Features. American Journal of Signal Processing, 5 (2): 32-39 DOI: 10.5923/j.ajsp.20150502.02.
[11]
F. Li., L. Xu, A. David and S. Clausi, (2015). Feature Extraction for Hyperspectral Imagery Via Ensemble Localized Manifold Learning. IEEE Geoscience And Remote Sensing Letters, Vol. 12, No. 12.
[12]
J. Kumari, R. Rajesh. and K. Pooja, (2015). Facial Expression Recognition: A survey. (Elsevier) Procedia Computer Science, 58 (2015) 486 – 491. www.sciencedirect.com.
[13]
K. Jain and H. Pann, (2017). Efficient Disparity Estimation From Stereo Images Using Hybrid- Guided Filter. The Imaging Science Journal, Volume 66, Issue 3. https://doi.org/10.1080/13682199.2017.1385893.
[14]
A. TeixeiraLopes, E. Aguiar, A. De Souza and T. Santos (2017). Facial Expression Recognition with Convolutionary Neural Network Coping With Few Data and Training Sample Order. Pattern Recognition, Volume 61, Pages 610-628.
[15]
S. Iloh and H. BongKang, (2018). Development And Utilization Of A Disgusting Image Dataset to Understand and Predict Visual Disgust. Image and Vision Computing, Volume 72, Pages 24-38.
[16]
S. Yixin, J. Liu and Du L. (2018). Wrong Matching Point Elimination after Scale Invariant Transformation and Its Application in Image Machinery. Pattern Recognition and Image Analysis. Volume 28, Issue 1, pp. 87–96.
[17]
C. Barker, L. Cox, J. Krinsky and G. Nilsen, (2013). Marxism and Social Movements. Konikelijke Brill NV. ISBN 978-90-04-21175-9.
[18]
Jameson (2014). Game Theory and Its Application. St. Catherine University, Sophia.
[19]
J. Chappello (2019). Matching Pennies. https:// www.investopedia.com/terms/m/matching-pennies.asp
[20]
M. Kress, J. Caulkins, G. Feichtinger, D. Grass and A. Seidl, (2017). Lanchester Model for Three-way Combat. Operations Research Department, Naval Postgraduate School, Monterey, California.
[21]
N. Perry, (2009). Fractal Effects in Lanchester Models of Combat. Department of Defense, Australia.
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